# Keycloak Example: Building My First MCP Server Tools With Quarkus
## Introduction
Learn to build an MCP server for Keycloak using Quarkus and Goose CLI.
* Entry for Beginner's Jam Summer 2025: a time-traveling tower defense game where you defend your base against waves of enemies using the power to rewind time, playable at https://m4v3k.itch.io/tower-of-time
Here is a comprehensive guide to working with multimedia data in Python:
**Multimedia Libraries**
Python has several multimedia libraries installable through pip, including:
* OpenCV: for accessing webcams and recording video
* Pygame: for playing sound files and recording images from webcams
* SoundDevice: for recording audio from the computer's microphone
* Wavio: for working with wave audio files
* Real-time object detection and understanding system powered by state-of-the-art AI models.
* Advanced analytics, intuitive interfaces, and enterprise-level performance monitoring.
* Support for multiple object classes including people, vehicles, animals, sports, food, furniture, electronics, kitchen tools, and more.
* Comprehensive analytics dashboard providing FPS monitoring, inference time, memory usage, detection accuracy, and performance trends over time.
* Real-time camera feed with live detection from webcam or IP cameras.
* Video file processing with batch processing of video files with progress tracking.
* Multi-camera support connecting and processing multiple camera streams.
## Models
* YOLOv8n: Latest state-of-the-art object detection with 30+ FPS performance.
* MobileNet-SSD: Lightweight model optimized for mobile and edge devices.
* ONNX Runtime: Cross-platform inference with optimal performance.
* EfficientDet-D0: Google's efficient detection with superior accuracy.
* A powerful Python library to create beautifully styled text images with a simple, fluent API.
* Powered by Google's Skia graphics engine.
* Makes it easy to generate styled text images for social media, video overlays, digital art, or any application where stylized text is needed.
* A Simple Twitter API Scraper
* You can use functions such as posting or searching for tweets without an API key using this library.
* Released twikit_grok an extension for using Grok AI with Twikit. https://github.com/d60/twikit_grok
cco provides essential protection while Claude Code is up close and personal with your system. It uses Docker as a barrier to keep Claude contained while keeping your real system safe.
* A simple notebook to do RAG based interrogation of congressional bills
Here is a comprehensive guide to writing style for technical documentation, based on the provided rules:
**General Guidelines**
1. **Be concise and clear**: Technical documentation should be easy to understand and free of unnecessary jargon.
2. **Use proper grammar and spelling**: Ensure that all text is grammatically correct and spelled correctly.
3. **Use proper punctuation**: Use commas, periods, semicolons, and other punctuation marks consistently throughout the document.
**Specific Guidelines**
1. **Subject-verb agreement**: Ensure that subject-verb agreement is used consistently throughout the document.
2. **Tense consistency**: Use a consistent tense (e.g., past, present, or future) when describing events.
3. **Use technical vocabulary correctly**: Use technical terms accurately and consistently throughout the document.
4. **Avoid contractions**: Avoid using contractions in technical documentation unless absolutely necessary.
5. **Use "I" sparingly**: Use the second person ("you") whenever possible to avoid using first-person pronouns (e.g., "I").
6. **Avoid passive voice**: Use active voice when describing actions and events.
7. **Use precise language**: Avoid using vague or ambiguous terms; instead, use specific and precise language to convey meaning.
**Style Conventions**
1. **Capitalization**: Capitalize the first word of each sentence and proper nouns (e.g., names of people, places, and organizations).
2. **Italicization**: Use italics for emphasis, such as for file paths or URLs.
3. **Bold formatting**: Use bold formatting to draw attention to important information, such as commands or options.
4. **Lists**: Use bullet points or numbered lists to present information in a clear and organized manner.
**Special Cases**
1. **Technical terms**: Use technical terms accurately and consistently throughout the document.
2. **Acronyms**: Define acronyms on their first use and use them consistently thereafter.
3. **Abbreviations**: Avoid using abbreviations unless absolutely necessary; if used, define them clearly.
4. **Examples**: Use examples to illustrate complex concepts or procedures.
**Red Flags**
1. **Ambiguous language**: Avoid using ambiguous terms that may be easily misinterpreted.
2. **Jargon overload**: Be cautious not to overwhelm the reader with too much technical jargon.
3. **Typos and errors**: Ensure that all text is error-free before publication.
4. **Outdated information**: Regularly review and update documentation to ensure accuracy and relevance.
* AI coding agent built for the terminal
* Install using `curl -fsSL https://opencode.ai/install | bash`
Context Forge is a tool that helps teams collaborate on software development projects by providing a set of pre-built templates, validation rules, and other features to ensure code quality and consistency.
**Key Features**
* **CLADE**: The main constitution file for your project, where you define the rules and guidelines for your development workflow.
* **Implementation Plan**: A breakdown of your development into manageable stages, with tasks and deliverables outlined in each stage.
* **Project Structure**: A definition of how your code should be organized, including folder structure and naming conventions.
* **Validation**: Context Forge includes a powerful validation system that ensures code quality, syntax, and testing consistency.
**Best Practices**
* Use `context-forge validate` to run all critical validations on your code before committing it.
* Regularly update Bug_tracking.md to document issues and bugs encountered during development.
* Run `context-forge validate --all` for comprehensive checks of your entire project.
* Add custom tech stack templates and validation commands to further customize the tool to your needs.
* A command-line and Python tool that transforms local codebases, GitHub repositories, web pages, and online documents into a single, context-rich prompt optimized for Large Language Models (LLMs)
* Supports compression, intelligent file filtering, multiple output formats, and in-depth analysis of your project
* Integrated system transforming Claude Code into an orchestrated development environment
* Automated documentation management, multi-agent workflows, and external AI expertise through Sub-Agents
* Overcomes challenges in scaling AI-assisted development:
* Managing architecture patterns and design decisions
* Ensuring coding standards and team conventions are followed
* Providing context in large codebases
* Solves problems with outdated library documentation, hallucinated API methods, and inconsistent architectural decisions
* Provides the "Four eyes principle" through MCP integration:
* Real-time library docs from Context7
* Architecture consultation from Gemini
* Ensures few errors, better code, and current standards
* Offers intelligent automation through hooks and commands:
* Automatic updates of documentation through custom commands
* Context injection for all Sub-agents and Gemini MCP calls
* Audio notifications for task completion (optional)
* One-command workflows for complex tasks
* Integrates battle-tested hooks for Claude Code's capabilities:
* Security Scanner
* Gemini Context Injector
* Subagent Context Injector
* Notification System
* Framework designed for adaptation:
* Commands - Modify orchestration patterns in `claude/commands`
* Documentation - Adjust tier structure for your architecture
* MCP Integration - Add additional servers for specialized expertise
* Hooks - Customize security patterns, add new hooks, or modify notifications in `claude/hooks`
* Sentient is an open-source AI project that aims to eliminate prompting and enable truly autonomous AI aligned with user goals.
* The platform features a journal page, tasks page, and web-based interface for deep integration and automation.
* Key features include:
* SuperMemory: permanent facts storage
* Notes & Journal: full-featured journal with contextual updates
* Generate Plans from Goals: detailed plans for task execution
* Asynchronous Execution: background handling of approved tasks
* View & Manage Tasks: dedicated tasks page with progress updates
Local Lens is a comprehensive tool for server log capture and analysis. Here's an overview of the project:
**Purpose**
Local Lens is designed to capture and analyze server logs from any backend framework, providing insights into application performance, security, and user behavior.
**Key Features**
1. **Server Capture**: Local Lens captures logs from servers running on localhost (port 27497).
2. **Log Analysis**: The tool analyzes captured logs to provide detailed insights into:
* Method and URL
* Request headers and bodies
* Response status codes and content
* Timing and latency
3. **Domain Filtering**: Local Lens allows users to specify domains or subdomains to capture log data from.
4. **MCP Integration**: The tool integrates with Model Context Protocol (MCP) for AI assistant access, enabling analysis and debugging of server logs.
5. **Structured Data**: Local Lens formats logs as JSON, providing a structured format for analysis and integration with other tools.
- Say "Hi" to Alice - your open-source AI companion designed to live on your desktop.
- Fast, VAD-powered voice recognition (via gpt-4o-transcribe or whisper-large-v3)
- Natural-sounding responses with OpenAI TTS
- Interruptible speech and streaming response cancellation for smoother flow
- Thoughts: Short-term context stored in Hnswlib vector DB
- Memories: Structured long-term facts in local DB
- Summarization: Compact message history into context prompts
- Emotion awareness: Summaries include mood estimation for more human responses
- Screenshot interpretation using Vision API
- Image generation using gpt-image-1
- Animated video states (standby / speaking / thinking)
- Interact with your local system with user-approved permissions:
- File system browsing (e.g. listing folders)
- Shell command execution (ls, mv, mkdir, etc)
- Granular command approvals: one-time, session-based, permanent
- Permissions settings tab
- Web search
- Google Calendar & Gmail integration
- Torrent search & download (via Jackett + qBittorrent)
- Time & date awareness
- Clipboard management
- Task scheduler (reminders and command execution)
- Open applications & URLs
- Image generation
- MCP server support
- Hierarchical Task Management via Model Context Protocol (MCP)
- Transform any MCP-compatible AI assistant into a powerful project manager with automated task breakdown, dependency tracking, and smart workflow management.
- Supported tools:
- Claude Code • Claude Desktop • Cursor • VS Code + Copilot • Continue.dev • Any MCP Client
- Key features:
- Complete User Guide - Comprehensive setup and usage guide
- Tools Reference - Complete MCP tools documentation
- AI Prompts - Ready-to-use prompts for task management
- Hierarchical Project Management: Ideas → Epics → Tasks with intelligent decomposition
- AI-Powered Task Creation: Automated breakdown using natural language prompts
- Smart Dependency Tracking: Automatic task sequencing and blocker detection
- NPX Ready: Zero-installation deployment with npx mcp-project-manager
- Interactive CLI Dashboard: Real-time project visualization and navigation
- Status Management: Pending → In-Progress → Done with progress tracking
- Approval Workflow: User control over all AI actions and modifications